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Metallic probe carriers are commonly used in magnetic flux leakage (MFL) inspection to support sensing elements and maintain lift-off, but a conductive carrier located near the sensor can act as an active electromagnetic boundary. This study investigates the carrier-induced waveform reconstruction caused by such a conductive near-field boundary. A theoretical model is developed to describe the induced current, secondary magnetic field, and relaxation-related downstream memory generated when the carrier moves through a non-uniform leakage field. Transient finite-element simulations are used to examine the effects of carrier material, scanning speed, and concave carrier geometry. Compared with the air reference, aluminum and copper carriers produce stage-dependent waveform reconstruction, including valley modification, peak modulation, feature-position shift, and trailing-side extension. The quantitative waveform-deviation indicators increase with increasing speed and are further regulated by carrier geometry. Experimental results based on repeated magnetic response events confirm amplitude suppression, non-zero residual after amplitude matching, response broadening, and enhanced trailing asymmetry. These results demonstrate that the metallic probe carrier is not an electromagnetically transparent holder but an active near-field conductive boundary that should be considered in probe-carrier design and MFL signal interpretation.

Supporting youth and their caregivers during the transition from pediatric to adult health care is a priority across Canada. Many transition in care (TiC) innovations exist, yet these innovations often fail to be effectively implemented and/or sustained. Knowledge translation (KT) interventions, such as developing educational materials and identifying champions, are used to promote the uptake of innovations into clinical practice. However, there is limited information on what, when, and how these KT interventions are used to implement and sustain TiC innovations. This paper presents the protocol for a realist review aiming to understand what KT interventions work, how they work, for whom, and under what circumstances to support the implementation and sustainability of TiC innovations for youth moving from pediatric to adult health care. The objectives are to (1) identify and map KT interventions and (2) develop initial program theories. We will follow Pawson's 5 iterative steps for realist reviews, integrating aspects of rapid realist review methodology. We will adhere to the RAMESES (Realist and Meta-narrative Evidence Syntheses-Evolving Standards) quality standards for realist synthesis. Using an integrated KT approach, we will leverage the lived and professional expertise of a team of knowledge users and researchers. This project has received funding from Canadian Institutes of Health Research, starting in January 2026. A research partner team of 24 people with lived and professional expertise in transition has been assembled, as well as a team of 18 scientific members. Step 1 of the review is underway. The review is anticipated to be completed within 12 months. Using a theory-driven approach, this realist review will result in initial program theories about the underlying mechanisms, contextual factors, and processes within KT interventions that influence implementation and sustainability outcomes of TiC innovations for youth and their caregivers. A subsequent explanatory mixed methods realist evaluation with a multiple comparative case study design will test and refine initial theories. This research will be important to inform future TiC innovations for diverse health contexts across Canada and beyond.

3D Gaussian Splatting (3DGS) has recently emerged as an effective representation for immersive 3D scene rendering, providing high visual fidelity and real-time rendering efficiency. To support interoperable compression of trained 3DGS content, the Moving Picture Experts Group (MPEG) is exploring Gaussian Splat Coding (GSC), which mainly targets already trained 3DGS models following the INRIA reference format. The current video-based GSC anchor reorders 3DGS attributes into 2D attribute maps using Parallel Assignment Linear Sorting (PLAS) and compresses the resulting maps using High Efficiency Video Coding (HEVC). However, higher-order spherical harmonic coefficients (SH-AC) often remain irregular and exhibit low local spatial correlation even after PLAS reordering, limiting the coding efficiency of conventional video codecs. This paper proposes a VQ-HEVC hybrid compression framework that is structurally compatible with the video-based GSC anchor framework, in which SH-AC coefficients are represented by vector quantization (VQ) indices, while the remaining attributes are encoded using the same HEVC-based procedure as the GSC anchor. The proposed method adopts a two-stage VQ scheme that combines coarse VQ and product-quantization-based residual quantization, together with zero-masked residual VQ and flexible PQ grouping, to improve index-map coding efficiency across rate points. The generated VQ indices are packed into YUV400 index-map sequences and encoded using HEVC lossless coding, while the corresponding codebooks are transmitted as metadata. Experimental results on the Bartender and Cinema sequences of the MPEG GSC CTC demonstrate consistent rate-distortion improvements over the video-based GSC anchor across multiple objective quality metrics within the evaluated setting. In terms of RGB-PSNR, the proposed method achieves BD-rate reductions of 22.3% and 18.5% for the Bartender and Cinema datasets, respectively. These results suggest that, for the evaluated GSC CTC sequences, VQ-based SH-AC representation can effectively complement PLAS-based video coding while maintaining consistency with the existing GSC coding structure.

Therapeutic decision-making for older adults, operationally defined in this review as patients aged ⩾65 years, with extensive-stage small-cell lung cancer (ES-SCLC) remains clinically challenging, complicated by heterogeneous decline in physiological reserve, multiple comorbidities, and narrow therapeutic indices. Although chemoimmunotherapy has redefined the first-line standard of care, the selection bias inherent in pivotal registrational randomized controlled trials-which often exclude the oldest-old and frail patients-limits the generalizability of their efficacy and safety profiles within complex real-world populations. As the therapeutic armamentarium expands, emerging modalities such as small-molecule anti-angiogenic agents, hypofractionated or adaptive radiotherapy, and novel molecular therapies (e.g., lurbinectedin and delta-like ligand 3-targeting constructs) offer tailored therapeutic avenues for this demographic. However, a pronounced evidence gap persists regarding geriatric-specific toxicity and functional endpoints within the existing evidence hierarchy. Consequently, the integration of Comprehensive Geriatric Assessment (CGA) for functional status stratification is imperative to navigate current therapeutic bottlenecks. The objective of this narrative review is to summarize recent therapeutic advances in older adults with ES-SCLC, critically examine the limitations of current evidence, and propose a CGA-guided framework for risk-adapted clinical decision-making and holistic management. A new treatment guide for older adults with advanced small-cell lung cancer: moving beyond age to personalized care Small-cell lung cancer is an aggressive disease that commonly affects older adults. Unfortunately, most clinical trials test new treatments on younger, healthier people, excluding those who are very old or frail. This creates a significant “evidence gap,” leaving doctors unsure of how to safely treat elderly patients who often have other serious health conditions, such as heart or lung disease. This paper reviews the latest medical advances and proposes a new way to solve this problem. We argue that a patient’s birth age should not be the only factor doctors use to make treatment decisions. Instead, we recommend using a detailed health check called a “Comprehensive Geriatric Assessment.” This assessment evaluates a patient’s physical strength, memory, and ability to perform daily activities, giving a clearer picture of their overall health. Based on this assessment, we provide a framework to classify patients into three groups: “Fit,” “Vulnerable,” or “Frail.” Fit patients are strong enough to receive standard, full-dose treatments. Vulnerable patients may receive adjusted treatments, such as lower chemotherapy doses, to reduce dangerous side effects. Frail patients may benefit most from care focused on comfort and maintaining quality of life rather than aggressive treatment. This personalized approach aims to give older adults the best chance of survival while protecting them from treatments that might be too harsh for their bodies.

Antimicrobial resistance (AMR) poses a critical global public health challenge, with animal gut microbiomes serving as significant reservoirs and transmission hubs for antimicrobial resistance genes (ARGs). This review synthesizes current knowledge on the central role of gut microbiomes in companion animals and livestock in facilitating AMR dissemination. It examines key mechanisms that enable horizontal gene transfer within intestinal ecosystems: conjugation, transduction, and transformation. It also highlights how co-selection by heavy metals, disinfectants, and other non-antibiotic agents sustains resistance even without direct antibiotic use. The review analyzes major drivers of AMR, including antimicrobial usage, husbandry practices, and environmental pressures. It critically evaluates microbiome-based interventions such as probiotics, postbiotics, and fecal microbiota transplantation. A distinctive contribution is the integration of these elements into a network-centric One Health framework that explicitly maps cross-species transmission pathways from livestock and companion animals to humans via direct contact, food chains, and environmental dissemination. By moving beyond descriptive cataloging to provide a mechanistic and ecological synthesis, this review aims to guide the development of targeted, microbiome-informed intervention and surveillance strategies.

This study employed Structural Topic Modeling (STM) on 56,526 Weibo posts to examine the evolving discourses surrounding the Fukushima wastewater release. While rational discourse focusing on technical and environmental concerns initially coexisted and competed with nationalist narratives, the discourse rapidly transformed into predominantly nationalist rhetoric driven by grassroots users. Nationalism discourse was framed through two antagonisms: 1) direct, volatile anti-Japanese sentiment rooted in historical grievances and 2) broader, consistent anti-Western skepticism anchored in geopolitical rivalries. Moving beyond the binary paradigm of Chinese cyber-nationalism as either purely top-down mobilization or bottom-up explosion, this study reveals a nuanced interplay between actor types: grassroots users predominantly led nationalistic discourses, especially anti-Japanese discourse, while Key Opinion Leaders (KOLs) focused on technical discussions but occasionally amplified anti-Western sentiment. By identifying a latent nationalist sensitivity and a victimhood-centric view of international affairs, the study demonstrates how affective public sentiment can transform environmental issues into perceived insults to national dignity, intensified by platform-mediated grassroots agency.

This work concentrates on the approximation and visualization of stochastic Poincaré maps for a slow-fast system with white noises. We utilize the Taylor formula along with a specialized moving orthogonal system to establish an expression and to visualize the shapes of the stochastic Poincaré maps for the slow-fast system. Furthermore, we derive an approximation of the stochastic Poincaré maps, which converge to the deterministic Poincaré maps as the singular perturbation and the noise perturbation approach zero. A specific example is given to demonstrate the behavior of stochastic Poincaré maps.

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This study focused on burn-through leakage at girth welds of mechanically lined pipe (MLP) during field service. Field failure analysis, experimental tests, and numerical simulation were combined to investigate the process parameters of seal welding and multi-pass girth butt welding. Macroscopic metallography and energy dispersive spectroscopy (EDS) of failed specimens showed that excessive welding heat input (high current) caused severe expansion of the heat-affected zone (HAZ) and significant element dilution. The results indicated that the HAZ width of the solid-wire girth weld increased markedly from 1.312 mm to 2.247 mm under high-current conditions. Meanwhile, the Fe mass fraction in the root pass sharply increased to 33.66%, while key corrosion-resistant elements such as Cr and Ni were greatly reduced, which directly led to local pitting corrosion and perforation leakage. In addition, a moving heat source model was established in Abaqus 2024 to simulate the multi-pass welding process. The results showed that strong stress concentration developed at the groove root and the interface between the backing steel pipe and corrosion-resistant liner during repeated thermal cycles. The maximum von Mises stress reached 686.56 MPa during the second butt welding pass. After final cooling, the residual hoop tensile stress and axial tensile stress at the center of the inner surface reached 500-550 MPa and 480-510 MPa, respectively. By correlating microscopic compositional evolution with the macroscopic residual stress field, this study revealed the weld failure mechanism of MLP joints. The proposed finite element method can also be used as an efficient tool to predict the effects of welding speed, current, and voltage on residual stress, providing guidance for field welding procedure optimization and pipeline structural integrity assessment.

Large-batch training is widely used to scale multimodal neural networks that integrate heterogeneous inputs such as visual, textual, and physiological signals. However, increasing the batch size suppresses the stochastic fluctuations of mini-batch sampling, which can trap multimodal models in sharp, modality-dominant minima and produce a persistent generalization gap. To address this problem, we propose Geometric Anisotropic Noise Injection (GANI), a curvature-aware optimization framework inspired by information geometry and multisensory integration. GANI decouples deterministic large-batch descent from stochastic geometric exploration. It approximates local curvature through an exponential moving average of first-order gradients and injects structured anisotropic noise during parameter updates, thereby restoring the geometry-aware exploration dynamics of small-batch stochastic gradient descent with linear computational complexity. Theoretical analysis shows that curvature-aligned stochasticity can accelerate escape from sharp modality-specific basins and guide parameters toward flatter regions. Experimental evaluations across multimodal benchmark settings and stress tests demonstrate that GANI reduces the generalization gap, improves convergence stability, bounds the maximum Hessian eigenvalue, and maintains stronger performance under visual noise and missing textual information than standard large-batch SGD and common adaptive optimizers. By linking optimization geometry with multimodal representation dynamics, GANI provides an efficient and interpretable mechanism for robust heterogeneous data processing. The framework offers potential value for uncertainty-aware multisensory integration, brain-inspired perception science, and scalable multimodal learning under noisy or incomplete sensory conditions.

Humans tend to attribute social characteristics to moving geometric shapes as if they were animate beings (eg, kissing each other while dancing). This ability is considered an integral component of day-to-day life and a fundamental key to navigating effective social communication. The Heider-and-Simmel (HS)-like paradigm is suitable for investigating mechanisms of social attribution across lifespans and population groups, particularly in clinical contexts. As vast majority of mental and neurodevelopmental disorders are gender-specific, the present study delves into possible gender impacts on inferring social interaction in HS-like movies. The outcome indicates that typically developing females and males do not differ in recognition accuracy and processing speed of social interaction in HS-like animations, though males take longer and tend to be less accurate in recognition of similar control movies, more often mistaking them for social interaction. The signal detection analysis shows that females tend to be more tuned to social interaction (in terms of sensitivity index, d') without gender difference in response bias (decision criterion c). In both genders, decision criteria are liberal (c&#xa0;<&#x2009;0), pointing to a tendency to anthropomorphize control animations. The outcome sheds light on the nature of gender impact on perception of interaction as well as on neural underpinnings of social attribution in the gendered brain.

Background/Objectives: Reliable detection of epileptic seizures using electroencephalography (EEG) is crucial for clinical diagnosis and for alleviating clinicians' workload. However, existing studies still make insufficient use of phase information, and the synergy between local time-frequency pattern extraction and global dependency modeling remains limited. Methods: We propose a seizure detection framework based on the continuous wavelet transform (CWT), a three-dimensional convolutional neural network (3D-CNN), and a vision transformer (ViT). First, multichannel EEG segments are preprocessed, after which CWT is used to generate power spectrograms and phase spectrograms. These representations are then fused along the depth dimension into a unified power-phase volume and fed into a hybrid network composed of a 3D-CNN feature extractor and a single-layer ViT encoder to jointly learn local time-frequency-channel coupling patterns and higher-level global dependencies. Finally, seizure detection is completed by combining moving-average filtering, thresholding, and collar correction. Results: On the public CHB-MIT dataset and the clinical SH-SDU dataset, the proposed method achieved average segment-level sensitivities of 98.68% and 92.05%, specificities of 98.33% and 97.53%, accuracies of 98.49% and 96.37%, and AUC values of 97.26% and 92.89%, respectively. In event-level evaluation, the average sensitivities were 99.13% and 96.08%, with false detection rates of 0.88/h and 0.69/h, respectively. Further multi-stage ablation experiments together with t-SNE and Grad-CAM visualizations provided qualitative and experimental support for the design rationale of the joint power-phase input and the hybrid 3D-CNN-ViT architecture. Conclusions: The proposed framework effectively exploits the complementary discriminative value of power and phase information in epileptic EEG and demonstrates strong detection performance under patient-specific evaluation on both public and clinically collected datasets.

Thorium is an extremely radioactive and hazardous material and there is increasing concern over the environmental impact of thorium on the health of soils. There have been a limited number of investigations into phytotoxicity, phytoremediation, and the mechanisms of thorium absorption by woody plants. The purpose of this review is to assess the impact of thorium on soil's physical, chemical, and biological characteristics and quantify how much thorium is available for woody plant uptake and how much thorium will be moved around within different parts of the plant. This review also examines the bioavailability, mobility and root uptake of thorium by woody plants. The extent to which woody plants absorb thorium has been reviewed according to three activity concentration rates: low, moderate and high. Furthermore, literature was reviewed and analyzed as to how woody plant absorption may be influenced by having elevated quantities of thorium in the soil, which exceeds the toxic threshold. Thorium adversely affects all living things through hindering seed germination, reducing plant growth, inhibiting photosynthetic rate, and reducing the plants' ability to absorb nutrients; thorium has not been demonstrated to have any biological function. Woody Plants can employ several methods to combat the negative effects of thorium, such as producing phytochelatins, utilizing multiple compartments for storing thorium, and producing many different types of enzymes that function as antioxidants. Therefore, there are numerous technologically based and microbially based methods available for remediating thorium-contaminated soils, one of which is the use of specific plant species as remedial agents. Finally, a thorough review of literature was performed to identify all native woody plants that are best adapted for phytoremediation of thorium-contaminated soils and the mechanisms used by these native woody plant species to protect themselves from the toxic effects of thorium contamination were determined.

Nucleic acid microarrays are widely used in many biological and medical applications. One of the critical steps in the nucleic acid microarray is the placement of nucleic acid molecules using molecular printing in designated spots on the substrate to produce molecular patterns. Molecular printing involves loading molecules onto a printer head and then mechanically moving the head to deposit them in the designed areas on the substrate. This study focuses on eliminating bulky mechanical and moving components in printing technologies, thereby enabling scalable molecular printing. We have employed low-frequency electric fields (0-10 MHz, 0-10 Vpp) to produce highly localized high-electric-field regions within a glass substrate patterned with two-dimensional (2D) gold electrodes. The experiments were conducted using short single- and double-stranded DNA molecules, and the DNA molecules were concentrated in high-electric-field regions. We have also studied the critical factors, such as dielectrophoresis, AC electroosmosis, and capillary flow, that affect DNA patterning. Our results show that electric-field-based molecular patterning is scalable and can be applied broadly to pattern a broad range of molecules, including DNA, mRNA, and proteins.

Temperament has been associated with reproductive success in beef cattle, with excitable animals often exhibiting reduced fertility. This study evaluated whether acclimating heifers to human handling during an ovulation synchronization protocol improves temperament and pregnancy rates to timed artificial insemination (TAI). A total of 622 Bos taurus yearling beef heifers across five locations and two breeding seasons (eight herd-year observations) were stratified according to reproductive maturity and temperament and were assigned to either acclimation (TRT; n = 307) or control (CTRL; n = 315). Acclimated heifers were moved through handling facilities without restraint prior to each protocol event (days 0, 7, and 10). Temperament was assessed using chute score (CS) and exit velocity (EV), and plasma cortisol was measured in a subset of animals. Acclimated heifers had lower CS on days 7 and 10 (p = 0.011 and p = 0.010, respectively) and greater pregnancy rates to TAI compared with control heifers (54.5% vs. 45.2%; p = 0.018). Exit velocity and cortisol concentrations did not differ between treatments (p &#x2265; 0.13). These results indicate that acclimation during handling events can improve behavioral responses and pregnancy rates to TAI with modest additional handling time (a mean of 17 s per heifer; no more than 18 min per location/day), providing a practical and scalable strategy for beef producers.

Carbon dioxide (CO&#x2082;) is one of the infamous greenhouse gases, resulting in increased global temperature and climate change. The steady rise in atmospheric CO&#x2082; levels, primarily driven by anthropogenic activities, poses serious environmental and socio-economic challenges. Understanding and forecasting CO&#x2082; emission trends are essential for guiding global mitigation efforts and assessing progress toward climate commitments. In this study, we aim to investigate the monthly CO&#x2082; emission trend from direct measurement data collected by the National Oceanic and Atmospheric Administration and build predictive models from three different modeling approaches: statistical: autoregressive integrated moving average, seasonal autoregressive integrated moving average; machine learning: Random Forest, adaptive boosting; and deep learning models: long short-term memory, gated recurrent unit. We constructed and trained multiple model configurations through a data-driven approach, then evaluated and selected a top-performing model from each category, enabling a robust performance comparison. Based on the experimental results on test data, the Random Forest model with 250 decision trees outperformed all other models with the best scores: root mean square error 0.2401, mean absolute percentage error 0.0005, and directional accuracy 0.9544. Forecasting was performed for the next 3 years from the top-performing models in each category. Experimenting from statistical to state-of-the-art deep learning models, this study serves as a baseline case study for developing advanced computational frameworks on emission data forecasting for the future. Overall, this research provides valuable tools and perspectives for climate scientists, stakeholders, and policymakers aiming to combat climate change through informed, predictive strategies.

Migraine and tension-type headache (TTH) are leading causes of neurological disability globally. While their macro-trends are documented, the nonlinear drivers and the regulatory role of socio-economic development across G20 countries remain under-explored. Based on Global Burden of Disease (GBD) 2023 data, the headache burden across G20 members was quantified. Developmental trajectories were identified by integrating the Socio-demographic Index (SDI). Crucially, a comparative analysis was conducted between a decomposition analysis model and an explainable machine learning framework, Shapley Additive Explanations-Extreme Gradient Boosting (SHAP-XGBoost), to quantify nonlinear marginal contributions and complex feature interactions. Finally, future trends from 2024 to 2035 were projected utilizing Autoregressive Integrated Moving Average (ARIMA) models. In 2023, the number of prevalent cases for migraine and TTH across G20 countries was estimated at 705 million and 1.27 billion, respectively. A significant nonlinear correlation between headache burden and the SDI was identified, with the burden for both disorders peaking at an SDI of approximately 0.8 before plateauing or declining. While population growth was identified as the primary macro-driver in both decomposition and SHAP analyses, a "saturation effect" within the headache models was revealed by SHAP analysis. Specifically, the marginal contribution of population size was found to diminish once specific thresholds were exceeded. Furthermore, according to ARIMA model projections, the age-standardized incidence rate (ASIR) of migraine is expected to stabilize in the coming years, whereas the ASIR of TTH is forecast to maintain a continuous upward trajectory through 2035. This study provides a comprehensive re-evaluation of migraine and TTH burden across G20 countries, shifting the focus from descriptive status quo to mechanistic explanation. Our findings confirm that while population growth remains a key contributor, variations in burden appear to be associated with differences in development level, age structure, and population scale.

"Ogres are like onions."1 This one phrase from the movie Shrek has always stuck with me, and not just because I can't stand onions. As I went through training, working with kids who have been through trauma or dealt with medical illness or other social stressors, it became even more apparent that people can be like onions as well. Different experiences can add layer upon layer, shaping how one interacts with the world. Someone might have been through experiences that have built defense mechanisms that make them seem like an ogre on the outside, when inside, they are just hiding from the traumas that they have seen. As child psychiatrists, much of our job is to help peel back these layers to better understand why the external layers exist. Why is a child refusing to do work? Is it just defiance, or a lack of motivation related to feeling depressed? Is it because they are struggling with focus due to ADHD and getting behind, so they just shut down? Building a safe space where children and adolescents can start to peel away these layers is an essential part of our jobs, and important in helping those around see and support the child moving forward.

GNSS/INS integrated navigation systems play a critical role in marine navigation, providing high-precision position and attitude information for moving platforms. However, in complex marine environments-such as occlusions caused by offshore engineering platforms-GNSS signal attenuation frequently leads to a rapid degradation of positioning accuracy. To address this issue in post-processing applications, this paper proposes an Adaptive Rauch-Tung-Striebel Smoother (ARTSS)-based GNSS/INS integrated navigation method. The proposed method first performs forward filtering using an Error-State Extended Kalman Filter (ESKF). Subsequently, an adaptive equivalent weight is dynamically constructed using the Huber M-estimation cost function based on the forward filtering innovations. This adaptive factor is utilized to dynamically modulate the smoothing gain in the backward pass, thereby effectively suppressing the interference of measurement outliers. To verify the effectiveness of the algorithm, comparative experiments are conducted using real-world land vehicle and shipborne kinematic datasets. Three methods are evaluated: the standard ESKF, the fixed-interval backward smoothing (RTSS), and the proposed ARTSS approach. The loosely coupled solutions from the Inertial Explorer (IE) software serve as the reference truth. Experimental results demonstrate that the proposed algorithm achieves significant improvements in positioning and attitude accuracy during GNSS signal outages. Specifically, compared with the conventional ESKF and RTSS methods, the 3D position accuracy of the shipborne experiment is improved by 31.07% and 6.97%, respectively, while that of the land vehicle experiment is increased by 48.05% and 8.67%. Therefore, the method presented in this paper effectively mitigates the accumulation of forward filtering errors and significantly enhances the accuracy, stability, and reliability of the integrated navigation system in complex environments.